How to Start Gen AI Training for Creative Teams: Steps & Expert Advice
Most creative leaders believe AI will accelerate production, but only a fraction of in-house teams have figured out how to integrate the tech into real workflows. This article explores how to train creative teams, what workflow integration looks like, and practical use cases that can help you embrace AI’s possibilities with confidence.
The creative industry has never moved this fast. AI image models improve every week, video tools get better by the day, and new cross-media creative tools are launched as we speak. For many overstretched creative teams, the pace feels exciting and overwhelming at the same time.
Most creative leaders know they need to adapt quickly, especially as AI-powered creative services become essential in modern design operations. But many don’t quite know how to tap into AI’s full potential.
Superside’s “The No-Hype AI Report” found that 96% of creative leaders believe AI will speed up production. Yet only 2% of in-house teams have successfully integrated AI into everyday workflows. The rest sit in the uncomfortable middle. They test tools, write prompts, take courses and explore platforms, but nothing really sticks. Output stays the same, and the workload doesn’t get lighter.
The real story behind GenAI’s impact on creative
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Most creative teams no longer need generic “AI 101” lessons. They need training that aligns with how creative work actually gets done. They need robust workflows, not theory. Tools, not trends. Feedback, not guesswork. And they need a mindset that treats AI as a creative multiplier instead of a threat.
This article breaks it all down. Read on to discover how to structure AI training for creatives, shift team culture, build confidence and truly integrate AI into creative execution, with insights from Tamara Dalhuijsen, Ariel Alejandro Ibañez and Júlio Aymoré.
The benefits of AI training for creative teams
When creative teams deeply understand how to use AI in their craft, results appear quickly. Production speeds up, consistency improves and incredible new ways of working open up.
Let’s take a closer look at the benefits.
1. Business benefits
AI training delivers clear operational gains. When creative teams successfully implement AI workflows in design processes:
- Turnaround times typically shrink from days to hours.
- Revision loops tighten because teams can iterate early and often.
- Design output increases without the help of traditional agencies, freelancers or additional staff.
- Brands gain access to greater creative variation and rapid ideation without additional time or cost investments.
2. Personal and team benefits
AI can relieve teams of the draining work that eats at energy and creativity.
When creatives, for example, use AI to automatically resize or retouch assets, they regain much of their mental bandwidth. More time can be devoted to refining ideas and outputs, which also improves morale and builds confidence.
Another positive effect? Deadlines feel more manageable, and creative work feels more rewarding.
3. Organizational benefits
When entire teams share AI fluency, everything syncs:
- Collaboration becomes smoother because everyone understands and uses the same tools.
- Communication gets clearer because expectations are aligned.
- Innovation becomes a natural part of the workflow instead of a side project.
- Teams adopt new models faster, cross-functional alignment improves and the entire creative culture becomes more innovative and adaptive.
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AI training challenges
The benefits are clear, but there’s a caveat: AI training for creatives isn’t plug-and-play. Many teams hit real emotional, technical and operational roadblocks along the way.
Superside’s AI leaders have seen these challenges play out across thousands of projects, and the patterns are remarkably consistent. Their experience shows that successful AI training depends on a team’s ability to overcome several key challenges.
Our experts believe it’s critical to:
1. Keep pace with a fast-changing ecosystem
The generative AI ecosystem evolves weekly, and static courses quickly become stale. Without a system that adapts as quickly as the technology does, skills decay, confidence drops and teams fall behind competitors who experiment and learn continuously.
Tamara Dalhuijsen, AI Enablement & Transformation Manager at Superside, explains the problem (and offers a solution):
AI evolves faster than traditional training can keep up. The only way to stay current is to teach teams how to learn in real time through the work itself.
2. Bridge the gap between customer expectations and creative capabilities
Customers increasingly expect AI-driven creative output, yet many in-house teams lack the fluency to deliver it. This amplifies the pressure and stress on already stretched operations.
The key is to make the training as practical as possible.
AI evolves faster than traditional training can keep up. The only way to stay current is to teach teams how to learn in real time through the work itself.
3. Overcome confidence and psychological barriers
Many creatives fear AI will replace or diminish their hard-earned skills. In fact, this fear is one of the biggest blockers to adoption.
Tamara recalls a designer who was initially intimidated by AI until she trained a custom AI image model with her own illustrative style.
She told me the moment she saw AI amplifying her voice instead of replacing it, everything changed. She felt more empowered, not less.
This emotional shift is essential. Without it, training usually remains surface-level and fails to translate into real adoption.
4. Shift from linear thinking to problem-solving
AI doesn’t fit neatly into traditional linear workflows. It requires nonlinear, lateral thinking and problem-solving.
Ariel Ibañez, Creative AI Enablement Senior Manager at Superside, notes that it requires a mental shift.
AI isn’t about mastering one tool. It’s about rethinking how we approach creativity altogether. It calls for a problem-solving mindset where technology becomes a creative partner.
When teams learn to move fluidly between ideation, prompting, refinement and manual polish, output improves dramatically.
5. Practice consistently
Fluency comes from repetition, which means one AI workshop simply isn’t enough.
Júlio Aymoré, Group Creative Director of Generative AI and AI-Powered R&D at Superside, says it mirrors the way you learn any craft.
You learn by observing, copying, drilling and then remixing. Expertise forms when people use AI regularly, not when they watch someone else use it.
We’ve witnessed this first-hand. When creative teams practice inside real projects, confidence compounds and AI adoption accelerates. The key is to create a strategic training plan that fosters creativity and curiosity, addresses fears about job security, and prioritizes hands-on experience.
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How AI training for creative teams actually works
AI creative training works best when it blends structured learning with hands-on experimentation. Teams need both the fundamentals (e.g., how the AI tools work, their limitations and how to prompt effectively) and real-world applications.
The best programs include onboarding, workflow integration, tool exploration, feedback loops and expert support. It’s essential to carefully consider both the format and the training topics.
In the case of Superside, this is how Tamara identified the need for broader, company-wide AI training:
We saw early on that customers wanted to bring AI into their projects, which made it clear we needed to train our creatives so they could use the technology with confidence. AI evolves too quickly for static courses, so we shifted to a hands-on, reactive learning model where creatives can follow real workflows and get expert support exactly when they need it.
The format of training sessions
The available interfaces and AI model behavior are two solid starting points. Once team members understand what the tools can (and can’t) do, the next step is to apply them to real workflows, actual assets, brand guidelines and customer scenarios.
Live demos, async walkthroughs, short explainer videos and small, hands-on workshops can give team members the creative space they need to experiment, troubleshoot and learn from one another.
Core training topics
To build real confidence with AI, every creative team should master five areas:
1. Foundational AI literacy
All creatives should learn how models interpret prompts.
Ariel calls this foundational skill “designing intentionally instead of prompting at random,” a shift that sits at the heart of effective AI creative training.
2. Workflow integration
Teams should learn where AI fits into the creative pipeline and how to use the tech for ideation, consistency, quality and speed at various points in the creative execution flow.
Creatives should also understand how to blend AI with human creativity and traditional skills to deliver truly exceptional work fast.
3. Brand consistency and quality control
AI tools make little sense if they don’t allow creatives to produce high-quality, brand-aligned work.
Designers and other team members should also learn to talk to AI in ways that produce the results they want, grounded in baked-in brand guidelines and guardrails.
4. Advanced tools and model stacking
Creative teams should also learn to combine models, use more advanced AI tools and integrate different types of inputs and outputs (such as text, images, audio, video or code) to create richer results.
The future of creative work is orchestration. When creatives learn to combine tools, they unlock results no single model can achieve alone.
5. Custom model development
Lastly, teams should learn to build and refine their brand-trained AI models. They need to know how to feed these models the correct data and use them directly inside tools like Figma to instantly generate on-brand assets.
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11 steps to build AI-ready creative teams
For the process to be genuinely effective, AI training for creatives shouldn’t be rushed or done half-heartedly. The framework below will create the conditions for long-term AI adoption.
Step 1: Assess AI readiness and identify your early adopters
Investigate where your team stands today: What do they already know? How comfortable are they with AI? Which workflows slow them down? Where are the most significant opportunities for support?
Look for the people who already use AI workflows in design processes. These early adopters can become internal champions who help spread skills, momentum and confidence across the team.
Step 2: Identify knowledge gaps across your creative team
Get a good grip on where team members get stuck, whether that’s prompting, data use, ethics or how to weave AI into everyday workflows.
Once you know where the knowledge gaps are, you can pull in the right experts to train your team.
People gain confidence when they use AI in real work with an expert guiding them. That’s when fear dissolves, and the learning really sticks.
Step 3: Create space for early, open dialogue
An early, open dialogue is essential, as it allows creatives to voice concerns, reset expectations and approach AI with curiosity rather than fear. This is where emotional resistance surfaces first, and where, as Júlio notes:
Introduce low-stakes forums where people can share anxieties, misconceptions and excitement without judgment. This could look like structured “ask me anything” sessions or small-group discussions facilitated by neutral moderators. Another idea is to start a dedicated group chat where people can drop links to articles, tutorials, tool reviews and quick tips.
Step 4: Remove the work that drains creativity
Ask your team which tasks feel repetitive or creatively uninspiring, and automate them with AI. Soon, your creatives will see AI adoption as support rather than a threat.
Step 5: Build a culture of experimentation
Create safe spaces where teams can try new things, share failures and swap discoveries. AI demo days can, for example, accelerate learning and adoption.
Consider, for example, weekly “sandbox hours” where employees can test workflows without producing anything.
Step 6: Apply the “see it, copy it, drill it, remix it” method
“Start small. Master one model. Practice with tight feedback. Then remix what you learn into your workflow,” Júlio advises.
When the learning process is broken down into these simple steps, it reduces overwhelm and builds the “muscle memory” needed to use AI instinctively during real projects.
Step 7: Teach context
Prompts only work well when creatives give the AI models the proper context (e.g., target audience, industry, brand guidelines).
Teach teams what information to provide or upload into their models so the AI can consistently produce strong, on-brand results.
Step 8: Run pilot projects
Launch small, low-risk AI creative endeavors that solve real problems in your workflow. Choose tasks where success is easy to measure (think speed, quality, consistency or reduced revisions).
When teams see the impact in their day-to-day work, momentum for broader adoption will grow naturally.
Step 9: Encourage the use of simple internal tools
Let teams build basic automations, custom GPTs or assistants. As they play with the tools, AI won’t feel as abstract anymore.
Over time, these simple tools can also become an internal ecosystem of AI solutions tailored to your team’s real needs.
Step 10: Define success and track progress
Set clear metrics and track whether AI adoption delivers on time saved, fewer revisions, faster turnaround, higher-quality outputs or increased creative capacity. Also track adoption rates to understand where your team might need more support.
Regularly review these insights with your team and leadership. Once everyone can see the positive impact, the shift to a culture of AI adoption becomes even easier.
Step 11: Secure leadership alignment
AI adoption typically scales only when leaders actively support the effort. They must champion the training, carve out time for teams to experiment and celebrate progress.
Struggling to get their buy-in? Frame experimentation in terms they care about: efficiency gains, competitive advantage, risk mitigation and creative differentiation. Leaders are far more likely to support experimentation when they can see measurable value early.
Be strategic with your AI integration
Our AI adoption guide gives you a human- and workflow-centric approach to empowering your team and finding the right places to start with your AI transformation.
Why choose Superside for AI training and creative team upskilling
Most companies want to adopt AI, yet few know how to turn that ambition into action. It’s not a straightforward process, and there’s no magic wand. But there is plenty of help out there.
As the world’s leading AI-powered creative service, Superside helps teams move from theory to practice with AI services that connect creative strategy, workflows, tools and hands-on coaching.
Unlike many traditional consultancies, we’ve already put AI upskilling into practice. Since we launched our AI-powered creative services in 2023, we’ve trained 95% of our global creative team, completed more than 5,000 AI-powered projects and enhanced over 45 creative workflows with artificial intelligence. In the process, we’ve saved our customers more than $4.5 million in creative costs.
We didn’t become an AI-powered creative team by reading manuals. We learned by doing. We tested every tool, refined every workflow and built the confidence to use AI inside real creative work. That’s why our training programs work. They’re shaped by what we’ve already lived through.
This internal transformation also serves as the foundation for how we can help you build an AI-ready creative team.
If you decide to Superside your AI training, our first step will be to assess your internal workflows, bottlenecks and capabilities. Then we’ll build a tailored roadmap that spans AI strategy, workflow redesign, tool prioritization, governance, data considerations and cross-team enablement.
The result will be a structured, long-term approach that embeds AI into your everyday operations rather than adding yet another tool to the stack.
Case study: How Sherweb turned AI curiosity into ROI
Cloud solutions provider Sherweb is one of the customers that benefited from this service.
When Sherweb needed to increase its creative output by 50%, we helped them leverage AI more consistently to achieve that. While a few early adopters were deep into experimentation, other Sherweb team members weren’t quite sure how to put the technology to good use. They also lacked shared workflows and AI toolkits, and there was no clarity about where AI could have the most significant operational impact.
Our team began with a comprehensive diagnostic process involving more than 30 stakeholders. We mapped Sherweb’s creative workflows, AI maturity and operational bottlenecks. This work ultimately became a readiness report that highlighted where AI could accelerate work, the skills required and who could serve as internal champions.
From here, Superside delivered more than 10 hours of guided, hands-on coaching. The sessions focused on everything from prompts and ideation to image generation, workflow design and AI-supported translation. Sherweb also implemented a prioritized toolkit of six AI tools, each with clear usage guidelines, to ensure outputs remained brand-safe and consistent.
Within a short time, Sherweb’s creative team began using AI in their day-to-day production. They also saw immediate gains in concept sketching, translations and long-form content drafting.
“In the past year, we’ve shifted the perception of the team and its output. Generative AI helped us accelerate and improve our delivery to keep up with the company’s demands.”
JP Mercier, Marketing Director at Sherweb
The shift was cultural as much as operational:
- AI became a standard creative practice rather than a side experiment.
- Many more team members experimented with AI and brought ideas to the table (unlike before).
- Internal champions helped spread adoption and ensured best practices circulated naturally.
- Leadership gained the momentum needed to scale AI across the business.
With Superside, you can benefit from custom workflows and models
Could a similar solution work for your business? Then work with a team that truly understands what AI training and successful adoption look like.
Superside has built over 40 AI-powered workflows that automate repetitive production tasks, improve consistency and reduce revision cycles. For more complex challenges, we also develop bespoke workflows aligned to our customers’ systems, data structure and brand guardrails. And we build custom AI image models trained on each brand’s visual language.
We’ve done this for a long list of customers, including:
Sailun Tire Americas: With a custom image model created by Superside and integrated into Figma, this brand turned a slow, manual production pipeline into a high-velocity creative engine. Their team now generates on-brand product visuals in minutes instead of hours. In fact, they’ve accelerated production by up to 10x and reduced per-image costs by 85%.
Maven Clinic: Maven partnered with Superside to build a custom lifestyle model that keeps every visual touchpoint cohesive. The model fuels brand books, long-form content, social creative and event materials. It gives the team warm, human-centered imagery on demand, without endless reshoots or inconsistent stock.
To see these custom models (and a couple more) in action, explore our ”No-Hype AI Report.”
Change management that supports people, not just processes
Through trial and error, we’ve learned that AI adoption succeeds when people feel confident, supported and safe to experiment. It also succeeds when leaders set clear expectations, communicate changes and build a supportive culture around experimentation and iteration.
If you need assistance, tap into Superside’s creative AI consulting services. We offer:
- 360-degree AI strategy and support
- Generative AI implementation
- Custom AI development
- Tailored upskilling and coaching
- Change management
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The future belongs to AI-ready creative teams
AI isn’t about to replace creative work. But it is reshaping creative industries.
The teams that thrive will be the ones who train early, adopt intentionally and build a culture that encourages experimentation. Strong training, combined with the right workflows, tools and custom models, unlocks measurable improvements in speed, quality and creative output.
If your team is ready to adopt or accelerate AI use in your creative processes, it’s time to Superside it. From hands-on training to custom models and workflow design, we’ll help you move from experimentation to excellence quickly.
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FAQs
Emanuel is a Content Specialist at Superside. With the knowledge that three languages (and counting) and digital marketing can serve a creator, he has helped B2Bs from multiple industries to write, optimize and scale their content game with compelling pieces that answers questions and solve problems. On Superside, Emanuel streamlines content ideas into powerful articles that guides you on how to use Superside multi-powered services to scale your business to the max.
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